5 plots Coding 1

# install.packages("tidyverse")
library(tidyverse)

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## v readr   2.1.2     v forcats 0.5.1

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airquality <- airquality

str(airquality)

## 'data.frame':    153 obs. of  6 variables:
##  $ Ozone  : int  41 36 12 18 NA 28 23 19 8 NA ...
##  $ Solar.R: int  190 118 149 313 NA NA 299 99 19 194 ...
##  $ Wind   : num  7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
##  $ Temp   : int  67 72 74 62 56 66 65 59 61 69 ...
##  $ Month  : int  5 5 5 5 5 5 5 5 5 5 ...
##  $ Day    : int  1 2 3 4 5 6 7 8 9 10 ...

airquality$Month[airquality$Month == 5]<- "May"
airquality$Month[airquality$Month == 6]<- "June"
airquality$Month[airquality$Month == 7]<- "July"
airquality$Month[airquality$Month == 8]<- "August"
airquality$Month[airquality$Month == 9]<- "September"

str(airquality)

## 'data.frame':    153 obs. of  6 variables:
##  $ Ozone  : int  41 36 12 18 NA 28 23 19 8 NA ...
##  $ Solar.R: int  190 118 149 313 NA NA 299 99 19 194 ...
##  $ Wind   : num  7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
##  $ Temp   : int  67 72 74 62 56 66 65 59 61 69 ...
##  $ Month  : chr  "May" "May" "May" "May" ...
##  $ Day    : int  1 2 3 4 5 6 7 8 9 10 ...

airquality$Month<-factor(airquality$Month, levels=c("May", "June","July", "August", "September"))

Plot 1: Create a histogram categorized by Month with qplot

p1 <- qplot(data = airquality,Temp,fill = Month,geom = "histogram", bins = 20)
p1

Plot 2: Make a histogram using ggplot

p2 <- airquality %>%
  ggplot(aes(x=Temp, fill=Month)) +
  geom_histogram(position="identity", alpha=0.8, binwidth = 5, color = "black")+
  scale_fill_discrete(name = "Month", labels = c("May", "June","July", "August", "September"))
p2

Plot 3: Create side-by-side boxplots categorized by Month

p3 <- airquality %>%
  ggplot(aes(Month, Temp, fill = Month)) + 
  ggtitle("Temperatures") +
  xlab("Monthly Temperatures") +
  ylab("Frequency") +
  geom_boxplot() +
  scale_fill_discrete(name = "Month", labels = c("May", "June","July", "August", "September"))
p3 

Plot 4: Make the same side-by-side boxplots, but in grey-scale

p4 <- airquality %>%
  ggplot(aes(Month, Temp, fill = Month)) + 
  ggtitle("Monthly Temperature Variations") +
  xlab("Monthly Temperatures") +
  ylab("Frequency") +
  geom_boxplot()+
  scale_fill_grey(name = "Month", labels = c("May", "June","July", "August", "September"))
p4

Plot 5: Scatterplot

ggplot(data = airquality) +
  ggtitle("Monthly Temperature/Wind Variations") +
  geom_point(mapping = aes(x = Temp, y = Wind, color = Month))

The scatterplot above depicts the correlation between the temperature and the wind speed measures for May through September. The scatterplot shows a negative association between wind and temperature. It also shows that May had the highest wind speed, while August had the lowest.